Project Summary/Abstract (30 lines) High physiological and cognitive workload required in de-coupled surgical work demands may have significant impact on patient outcome, surgical efficacy, and surgical performance. As novel surgical techniques, e.g., telesurgery, are developed, surgical operations will become more complex and the mental and physical demand on surgeons will likely increase, making it critical to develop remote and connected workload monitoring methods for the safe and effective surgical procedure design, testing, and training. This work will implement novel technology and machine learning analytics to quantify real-time and remote workload and test how workload feedback can impact care delivery in both in telesurgery and surgical simulation environments. Our overall hypothesis is that connected sensing technology in telesurgical procedures and simulation can improve surgical training and understanding of the impact of their workload on performance; ultimately improving patient health, surgery efficacy, and patient access (e.g., tele-mentoring) to surgical care. Two specific aims are proposed to investigate this hypothesis. The objective of Specific Aim 1 is to develop a connected sensor system to objectively quantify workload real-time in simulated telerobotic procedures. This involves: 1) integrating non-intrusive sensors into a single system within the simulation trainer or environment, 2) training machine learning techniques to objectively distinguish workload using a simulated surgical skills tasks, and 3) validating metrics across varying levels of cognitive loads under various task difficulty with medical trainees and expert participants. The objective of Specific Aim 2 is to determine the impact of the real-time workload feedback intervention on trainee performance times, errors, and intraoperative workload. Two tasks are proposed: 1) Explore modalities preferred by surgeons for providing real-time feedback on workload and 2) Assess impact of workload feedback on task performance and learning. Our primary hypothesis is that performance times and errors will improve when participants are provided realtime feedback on workload compared to performance with no feedback. The expected deliverables include 1) workload monitoring technology, algorithms, and software for complementing current simulation-based training, 2) objective and automated workload metrics, 3) real-time assistive intervention tool, and 4) preliminary evidence on impact of workload monitoring on training. The technology in this proposed work will improve public health by reducing adverse events due to human factors in surgery and improve access to surgical care with intervention technology that can adaptively train surgeons and remotely assess proficiency.